This thesis work is explaining about the parameter optimization of chipboard manufacturing process from eucalyptus and water hyacinth. Chipboard is a panel material manufactured under pressure from particles of wood and other lingo-cellulosic material with or without addition of additive(s). Eucalyptus and water hyacinth chips are being reinforced with resin in different contents of 20%, 25% and 30% by applying 120, 140, and 160degree Celsius with pressure of 5, 6, and 7MPa for 2, 3, and 4 minutes duration. It presents an effective approach for the optimization of process parameters for chipboard production using Minitab 19 and Taguchi-Grey Relational Analysis method in varying condition. An experimental study has to be conducted to come out with an optimum outcome. The chipboard production parameters namely material, resin, temperature, pressure, and press time are optimized with multiple performance characteristics, such as high modulus of elasticity and modulus of rupture, and low thickness swelling and water absorption. Analysis of variance (ANOVA), Taguchi and Grey Relational Analysis were carried out to obtain the significant factors. The response table and response graph for each level of chipboard production parameters are obtained from the Taguchi method and the optimum levels of manufacturing parameters are being selected. From the analysis, it was observed that eucalyptus material, pressure, and resin are consecutively the best significant parameters. From the experimental result, the thickness swelling at 2hours is 1.38% and 2.31% at 24hours. The value of water absorption of chipboard at 2hours and 24 hours are 10.32% and 12.45% respectively. The result of MOE is 2962.57MPa and 20.74MPa for MOR. All experiments are performed at 95% confidence level. A confirmation test was carried out to validate the optimized results and there have approximate results in S/N ratios from the initial to the optimal setting. Key words: resin based composite, eucalyptus and water hyacinth chips, Taguchi method, ANOVA, Grey Relational Analysis, and Optimization.